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Zoekresultaten (85)

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HBO/WO

MBO

6 november 2021

Julia Data Science

This is an open-source and open access book on how to do Data Science using Julia. The book describes the basics of the Julia programming language DataFrames.jl for data manipulation and Makie.jl for data visualization. You will learn to: Read CSV and Excel data into Julia Process data in Julia, that is, learn how to answer data questions Filter and subset data Handle missing data Join multiple data sources together Group and summarize data Export data out of Julia to CSV and Excel files Plot data with different Makie.jl backends Save visualizations in several formats such as PNG or PDF Use different plotting functions to make diverse data visualizations Customize visualizations with attributes Use and create new plotting themes Add LaTeX elements to plots Manipulate color and palettes Create complex figure layouts

Open (Access) Tekst Boek

Jose Storopoli, Rik Huijzer, Lazaro Alonso

1 februari 2023

Introduction to data science

This e-learning series is intended as a basic introduction to data science. It is aimed particularly at veterinary undergraduates, but may also be useful for others. The topics are: Topic 1: basic principles of entering, storing and using data Topic 2: introduction to reading a dataset into R Topic 3: introduction to working with datasets in R Topic 4: introduction to creating summary statistics and plots in R The videos provided here serve as an introduction to the online exercises and self-assessment exercises found at https://www.costmodds.org/teaching/voc_data_science/ Installation of the freely available software R (https://cran.r-project.org) is required to complete practical exercises 2-4.

Website

Matt Denwood

2 september 2025

Intro to Open Science

Created for the GSLS Master introduction Days 2025, Utrecht University. This lecture will introduce you to the principles and practices of Open Science, a global movement aimed at making scientific research more accessible, transparent, and collaborative. You will learn why open science matters for accelerating discoveries, improving research quality, and enhancing reproducibility. Key topics include open access publishing, open and FAIR data, preprints, open peer review, and sharing research tools and methods. The session will also cover practical tips on how you can integrate open science practices into your own research from the start of your graduate journey. By embracing open science, you contribute to a more inclusive and impactful scientific community.

Presentatie

Annetrude de Mooij-van Malsen

2 maart 2024

Data management

Introductie op research data management, met onder meer aandacht voor Open Science, FAIR data management, een aantal praktische tips en persoonlijke data. De slides zijn voorzien van het UvA-logo, en een aantal slides hebben betrekking op de lokale situatie van de UvA.

Presentatie

Casper Fermin, Joost Oosterhuis, Ira Lenters

1 augustus 2026

Introduction to data management as part of Open Science

Data management is part of the research cycle. It is crucial to think about it in the design phase of any experiment. This self-study module gives you hands on advice on what to do (and what NOT) with research data. It is not about (statistical) data analysis. This self-study module helps students get familiar with setting up their data management plan. As it’s an introduction to this topic, it is for students at beginner level. After completing this self-study module, you should be able to answer the following questions: What do I store? Where do I store? How can others understand what I stored? Why should I care?

Website

Susanne Kirchhoff

2024

Workshop FAIR data [january 2024]

This presentation contains the following topics: Open Science Looking back at the Data Management Workshop FAIR F – Findability A- Accessiblity I- Interoperability R- Reuseability

Presentatie

Ingrid van van Gorkum

25 oktober 2017

What is Data Science?

PowerPoint presentation about the question 'What is Data Science?'

Document

Stimuleringsregeling Open en Online Onderwijs

1 januari 1970

Introduction to Data Science

Introduction to Data Science; Classification, regression, basic concepts, correlations, spurious correlations, decision tree, description vs prediction, WEKA file format and essentials.

Document

Auteur onbekend

10 augustus 2023

Research Data Management: Open Data

What is Open Data? Open Data is data that anyone can access, use and share. Open Data challenges intelligent and creative minds to provide societal value. Open Data applications allow us to make better use of existing resources and create new products and services. Open Data is also one of the cornerstones of Open Science, a concept central to science practitioners and policy makers of today, promoting (re)use of research data and results amongst scientists, the business community and society as a whole. Open Data and academic research In academia, Open Data are used in humanities and cultural heritage research communities (e.g. Journal of Open Humanities Data (JOHD), Europeana); Open Data are also common in the life sciences (e.g. Elixer) and in economics (e.g. World Bank). Some open datasets can be useful to explore societally relevant questions relevant to current policy debates and topics of interest to specific demographics. Open Data is also important for educational purposes (e.g., see Bishop & Kuula-Luumi, 2017). Open Data applications Open Data helps to create (new) value for societies, citizens and businesses. There are now several examples of successful startups with innovative applications of Open Data. Appealing examples are available in health, transport, energy and the environmental sector (see ODINE for examples). I want to work with Open Data. Where to start, who to contact? A good starting point for information and inspiration on “all things” Open Data is the Open Data Handbook. This online handbook provides case studies, learning resources and networked information on Open Data. Related. Support.

Website

Erasmus University Library

2023

Workshop FAIR Data [ april 2023]

The FAIR principles contain guidelines for good data management practices that aim at making data FAIR: findable, accessible, interoperable, and reusable. In this presentation these four foundational principles (findability, accessibility, interoperability, and reusability) are explained with practical examples.

Presentatie

Ingrid van van Gorkum, Jellie Visser